Generally, the detailed mechanisms by which most genes, cells and viruses function in humans and other organisms are relatively unknown, despite the fact that there have been successful genomic sequencing programs and other extensive functional genomics efforts. Consequently, there is a need for screening approaches that enable high-throughput examination and elucidation of gene functions in a relevant physiological environment.
High-throughput, automated fluorescence microscopy makes it possible to screen and analyze the functions of hundreds of thousands of gene products in the context of the cell. Because microscopy has the potential to yield significantly more sub-cellular information than other detection methods, the term ‘high content’ has been adopted to describe screens and assays that are detected and quantified using automated imaging platforms. Both image acquisition and image analysis can be automated and then optimized to maximize throughput on high-content analysis systems. Two core components of the computational tools required for automated image analysis are the segmentation and positional tracking of individual cells.
Motility and division are two fundamental cell behaviors that are of great importance in a number of areas of research, including oncology, immunology, and developmental biology. While these behaviors have unique analysis requirements, they also share several significant analysis challenges. Examination of these behaviors requires time-lapse imaging. Implementation of time-lapse imaging for large-scale experimentation poses challenges with respect to finding assay, imaging, and analysis parameters that will be optimal across the wide variety of treatment conditions and behavioral phenotypes represented in a particular data set. Another challenge is that confluent cells/nuclei are commonly observed in microscopy images. During segmentation of these images, the confluent cells get correctly separated from the background as shown in FIG. 4A, but are not separated from each other, as shown in FIG. 4B. These clump of cells, which looks like a single unit can induce errors in measurements conducted on cells, like area, count of cells etc.
There is a need for a system and method that is able to separate confluent cells from each other so that there are no induced errors in measurements conducted on cells, like area, count of cells etc.